… intracluster load balancing afterwards. Second, for the intracluster load balancing, this article
proposes an off-policy DRL-based MLB algorithm to autonomously learn the optimal MLB …

… The originality of this paper is to introduce a load balancing and optimization strategy (LBOS)
using dynamic resource allocation method based on reinforcement learning and genetic …

… In this paper, the load balancing problem has been addressed under the constraint of achieving
… load balancing algorithm has been proposed using a reinforcement learning technique. …

… problems is to make load balancing among these base stations. … reinforcement learning
approach, called ORLA. It is a distributed user association algorithm for network load balancing …

… Moreover, none of them has ever studied the problem of load balancing as we have described.
In … Therefore, it can significantly improve the load balancing of data communication and …

… Traditional load balancing approaches are usually the … , a deep reinforcement learning
(DRL)-based load balancing … (DDPG) algorithm to adaptively learn the link-weight values by …

… In this paper, we presented a reinforcement learning startegy to achieving load balancing
in LTE cellular networks. In order to simulate the LTE environment, we used NS-3, a high-level …

… In this work we addressed the problem of load balancing and resource management by
considering a reinforcement learning method that relies on a MOSS model to allow the method …

… and unbalanced loads. … load-balancing architecture based on reinforcement learning (ALBRL)
in SDN. In this architecture, we design a load-balancing optimization model in high-load …

… According to the load balancing mechanism based on reinforcement learning (RL-LBM)
proposed in this paper, two typical mechanisms of load balancing are selected as the …